yee

gistfile1.py

def rolling_apply_slow(a, w):
    print len(a)
    """applys a function to a window slowly b/c python loops != c loops"""
    r = np.empty(a.shape)
    r.fill(np.nan)
    for i in range(w-1, a.shape[0]):
        r[i] = max(a[(i-w+1):i+1])
    print len(r)
    return r

def rolling_window(data, window):
    """way faster, applys a strided (moving) window over the array"""
    shape = data.shape[:-1] + (data.shape[-1] - window + 1, window)
    strides = data.strides + (data.strides[-1],)
    return np.lib.stride_tricks.as_strided(data, shape=shape, strides=strides)

y = lowess_smooth(xdata, ydata, .25, 2)
m = np.amax(rolling_window(y, 2*w+1), 1)                         

idx = np.delete(y, [(n-1) - i for i in xrange(len(y[:w]))], None)
delta = np.array((filter(lambda x: x not in idx[:w], idx)))
delta = m - delta